Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We evaluate BURBERRY GROUP PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Independent T-Test1,2,3,4 and conclude that the LON:BRBY stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:BRBY stock.
Keywords: LON:BRBY, BURBERRY GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Trading Signals
- How useful are statistical predictions?
- Fundemental Analysis with Algorithmic Trading
LON:BRBY Target Price Prediction Modeling Methodology
Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. We consider BURBERRY GROUP PLC Stock Decision Process with Independent T-Test where A is the set of discrete actions of LON:BRBY stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4
F(Independent T-Test)5,6,7= X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of LON:BRBY stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:BRBY Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LON:BRBY BURBERRY GROUP PLC
Time series to forecast n: 17 Oct 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:BRBY stock.
X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)
Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)
Z axis (Yellow to Green): *Technical Analysis%
Conclusions
BURBERRY GROUP PLC assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Independent T-Test1,2,3,4 and conclude that the LON:BRBY stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:BRBY stock.
Financial State Forecast for LON:BRBY Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | B2 |
| Operational Risk | 39 | 36 |
| Market Risk | 45 | 53 |
| Technical Analysis | 88 | 44 |
| Fundamental Analysis | 85 | 35 |
| Risk Unsystematic | 66 | 74 |
Prediction Confidence Score
References
- Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
- Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
- Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
Frequently Asked Questions
Q: What is the prediction methodology for LON:BRBY stock?A: LON:BRBY stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Independent T-Test
Q: Is LON:BRBY stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:BRBY Stock.
Q: Is BURBERRY GROUP PLC stock a good investment?
A: The consensus rating for BURBERRY GROUP PLC is Hold and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:BRBY stock?
A: The consensus rating for LON:BRBY is Hold.
Q: What is the prediction period for LON:BRBY stock?
A: The prediction period for LON:BRBY is (n+1 year)
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